{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current_dir=/home/rafaels/ml-agents/lib/python3.7/site-packages/pybullet_envs/bullet\n",
      "device:  cpu\n",
      "urdf_root=/home/rafaels/ml-agents/lib/python3.7/site-packages/pybullet_data\n",
      "options= \n",
      "max_steps:  1000\n",
      "device:  cpu\n",
      "leraning rate:  3e-05\n",
      "state_dim:  28 , action_dim:  8\n",
      "threshold:  5.0\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import pybullet_envs\n",
    "import pybullet_envs.bullet as bul\n",
    "import numpy as np\n",
    "import torch\n",
    "from sac_agent import soft_actor_critic_agent, device\n",
    "from replay_memory import ReplayMemory\n",
    "\n",
    "seed=0  \n",
    "## env = gym.make('MinitaurBulletEnv-v0')\n",
    "env = gym.make('MinitaurBulletDuckEnv-v0')\n",
    "torch.manual_seed(seed)\n",
    "np.random.seed(seed)\n",
    "env.seed(seed)\n",
    "max_steps = env._max_episode_steps\n",
    "print('max_steps: ', max_steps)\n",
    "\n",
    "batch_size=128 ##  512 \n",
    "LEARNING_RATE=0.00003 \n",
    "start_steps=10000 ## Steps sampling random actions\n",
    "replay_size=1000000 ## size of replay buffer\n",
    "\n",
    "agent = soft_actor_critic_agent(env.observation_space.shape[0], env.action_space, \\\n",
    "        hidden_size=420, seed=seed, lr=LEARNING_RATE, gamma=0.99, tau=0.005, alpha=0.2)\n",
    "##        hidden_size=256, seed=seed, lr=LEARNING_RATE, gamma=0.99, tau=0.005, alpha=0.2)\n",
    "\n",
    "\n",
    "memory = ReplayMemory(replay_size)\n",
    "\n",
    "print('device: ', device)\n",
    "print('leraning rate: ', LEARNING_RATE)\n",
    "\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.shape[0]\n",
    "\n",
    "print('state_dim: ',state_dim, ', action_dim: ', action_dim)\n",
    "\n",
    "threshold = env.spec.reward_threshold\n",
    "print('threshold: ', threshold)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save(agent, directory, filename, episode, reward):\n",
    "    torch.save(agent.policy.state_dict(), '%s/%s_actor_%s_%s.pth' % (directory, filename, episode, reward))\n",
    "    torch.save(agent.critic.state_dict(), '%s/%s_critic_%s_%s.pth' % (directory, filename, episode, reward))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ep.: 10, Tot.St.: 4198, Avg.Num.St.: 381.6, Min-Max.Sc.: (-1.39, 0.02), Avg.Score: -0.564, Time: 00:02:42\n",
      "Ep.: 20, Tot.St.: 7816, Avg.Num.St.: 372.2, Min-Max.Sc.: (-1.39, 0.02), Avg.Score: -0.553, Time: 00:05:05\n",
      "Ep.: 30, Tot.St.: 10981, Avg.Num.St.: 354.2, Min-Max.Sc.: (-1.39, 0.02), Avg.Score: -0.527, Time: 00:07:10\n",
      "Ep.: 40, Tot.St.: 13052, Avg.Num.St.: 318.3, Min-Max.Sc.: (-1.39, 0.02), Avg.Score: -0.470, Time: 00:08:35\n",
      "Ep.: 50, Tot.St.: 15337, Avg.Num.St.: 300.7, Min-Max.Sc.: (-1.39, 0.02), Avg.Score: -0.440, Time: 00:10:07\n",
      "Ep.: 60, Tot.St.: 16953, Avg.Num.St.: 277.9, Min-Max.Sc.: (-1.39, 0.04), Avg.Score: -0.399, Time: 00:11:12\n",
      "Ep.: 70, Tot.St.: 19464, Avg.Num.St.: 274.1, Min-Max.Sc.: (-1.39, 0.04), Avg.Score: -0.403, Time: 00:12:52\n",
      "Ep.: 80, Tot.St.: 22006, Avg.Num.St.: 271.7, Min-Max.Sc.: (-1.39, 0.04), Avg.Score: -0.408, Time: 00:14:34\n",
      "Ep.: 90, Tot.St.: 24415, Avg.Num.St.: 268.3, Min-Max.Sc.: (-1.41, 0.04), Avg.Score: -0.415, Time: 00:16:11\n",
      "Ep.: 100, Tot.St.: 27572, Avg.Num.St.: 272.8, Min-Max.Sc.: (-1.70, 0.04), Avg.Score: -0.442, Time: 00:18:18\n",
      "Ep.: 110, Tot.St.: 30534, Avg.Num.St.: 263.4, Min-Max.Sc.: (-2.32, 0.04), Avg.Score: -0.458, Time: 00:20:17\n",
      "Ep.: 120, Tot.St.: 33655, Avg.Num.St.: 258.4, Min-Max.Sc.: (-2.32, 0.04), Avg.Score: -0.486, Time: 00:22:23\n",
      "Ep.: 130, Tot.St.: 37053, Avg.Num.St.: 260.7, Min-Max.Sc.: (-2.32, 0.04), Avg.Score: -0.503, Time: 00:24:38\n",
      "Ep.: 140, Tot.St.: 40317, Avg.Num.St.: 272.6, Min-Max.Sc.: (-2.32, 0.04), Avg.Score: -0.520, Time: 00:26:49\n",
      "Ep.: 150, Tot.St.: 42146, Avg.Num.St.: 268.1, Min-Max.Sc.: (-2.32, 0.04), Avg.Score: -0.507, Time: 00:28:02\n",
      "Ep.: 160, Tot.St.: 44450, Avg.Num.St.: 275.0, Min-Max.Sc.: (-2.32, 0.03), Avg.Score: -0.529, Time: 00:29:34\n",
      "Ep.: 170, Tot.St.: 47280, Avg.Num.St.: 278.2, Min-Max.Sc.: (-2.32, 0.03), Avg.Score: -0.547, Time: 00:31:27\n",
      "Ep.: 180, Tot.St.: 49001, Avg.Num.St.: 269.9, Min-Max.Sc.: (-2.32, 0.12), Avg.Score: -0.542, Time: 00:32:35\n",
      "Ep.: 190, Tot.St.: 51834, Avg.Num.St.: 274.2, Min-Max.Sc.: (-2.32, 0.12), Avg.Score: -0.569, Time: 00:34:27\n",
      "Ep.: 200, Tot.St.: 54143, Avg.Num.St.: 265.7, Min-Max.Sc.: (-2.32, 0.12), Avg.Score: -0.585, Time: 00:36:00\n",
      "Ep.: 210, Tot.St.: 56658, Avg.Num.St.: 261.2, Min-Max.Sc.: (-2.02, 0.12), Avg.Score: -0.625, Time: 00:37:40\n",
      "Ep.: 220, Tot.St.: 59236, Avg.Num.St.: 255.8, Min-Max.Sc.: (-2.06, 0.12), Avg.Score: -0.639, Time: 00:39:23\n",
      "Ep.: 230, Tot.St.: 62461, Avg.Num.St.: 254.1, Min-Max.Sc.: (-2.54, 0.12), Avg.Score: -0.681, Time: 00:41:32\n",
      "Ep.: 240, Tot.St.: 64460, Avg.Num.St.: 241.4, Min-Max.Sc.: (-2.54, 0.12), Avg.Score: -0.697, Time: 00:42:52\n",
      "Ep.: 250, Tot.St.: 67091, Avg.Num.St.: 249.4, Min-Max.Sc.: (-2.54, 0.12), Avg.Score: -0.737, Time: 00:44:37\n",
      "Ep.: 260, Tot.St.: 68974, Avg.Num.St.: 245.2, Min-Max.Sc.: (-2.54, 0.12), Avg.Score: -0.733, Time: 00:45:53\n",
      "Ep.: 270, Tot.St.: 70996, Avg.Num.St.: 237.2, Min-Max.Sc.: (-2.60, 0.12), Avg.Score: -0.742, Time: 00:47:14\n",
      "Ep.: 280, Tot.St.: 73364, Avg.Num.St.: 243.6, Min-Max.Sc.: (-2.60, -0.01), Avg.Score: -0.758, Time: 00:48:49\n",
      "Ep.: 290, Tot.St.: 75229, Avg.Num.St.: 233.9, Min-Max.Sc.: (-2.60, 0.17), Avg.Score: -0.712, Time: 00:50:04\n",
      "Ep.: 300, Tot.St.: 78643, Avg.Num.St.: 245.0, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.729, Time: 00:52:21\n",
      "Ep.: 310, Tot.St.: 80805, Avg.Num.St.: 241.5, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.681, Time: 00:53:48\n",
      "Ep.: 320, Tot.St.: 83969, Avg.Num.St.: 247.3, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.647, Time: 00:55:56\n",
      "Ep.: 330, Tot.St.: 87936, Avg.Num.St.: 254.8, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.661, Time: 00:58:34\n",
      "Ep.: 340, Tot.St.: 90031, Avg.Num.St.: 255.7, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.660, Time: 01:00:00\n",
      "Ep.: 350, Tot.St.: 93013, Avg.Num.St.: 259.2, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.684, Time: 01:02:00\n",
      "Ep.: 360, Tot.St.: 96609, Avg.Num.St.: 276.4, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.758, Time: 01:04:25\n",
      "Ep.: 370, Tot.St.: 98981, Avg.Num.St.: 279.9, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.752, Time: 01:06:00\n",
      "Ep.: 380, Tot.St.: 102100, Avg.Num.St.: 287.4, Min-Max.Sc.: (-2.79, 0.17), Avg.Score: -0.791, Time: 01:08:06\n",
      "Ep.: 390, Tot.St.: 104110, Avg.Num.St.: 288.8, Min-Max.Sc.: (-2.79, -0.06), Avg.Score: -0.820, Time: 01:09:28\n",
      "Ep.: 400, Tot.St.: 106663, Avg.Num.St.: 280.2, Min-Max.Sc.: (-2.68, -0.06), Avg.Score: -0.801, Time: 01:11:11\n",
      "Ep.: 410, Tot.St.: 109100, Avg.Num.St.: 282.9, Min-Max.Sc.: (-2.68, -0.06), Avg.Score: -0.808, Time: 01:12:50\n",
      "Ep.: 420, Tot.St.: 111537, Avg.Num.St.: 275.7, Min-Max.Sc.: (-2.68, -0.06), Avg.Score: -0.808, Time: 01:14:28\n",
      "Ep.: 430, Tot.St.: 114179, Avg.Num.St.: 262.4, Min-Max.Sc.: (-2.49, -0.06), Avg.Score: -0.763, Time: 01:16:15\n",
      "Ep.: 440, Tot.St.: 116829, Avg.Num.St.: 268.0, Min-Max.Sc.: (-2.49, -0.10), Avg.Score: -0.767, Time: 01:18:01\n",
      "Ep.: 450, Tot.St.: 119874, Avg.Num.St.: 268.6, Min-Max.Sc.: (-2.49, 0.02), Avg.Score: -0.760, Time: 01:20:04\n",
      "Ep.: 460, Tot.St.: 122782, Avg.Num.St.: 261.7, Min-Max.Sc.: (-2.49, 0.02), Avg.Score: -0.746, Time: 01:22:01\n",
      "Ep.: 470, Tot.St.: 126681, Avg.Num.St.: 277.0, Min-Max.Sc.: (-2.49, 0.02), Avg.Score: -0.772, Time: 01:24:37\n",
      "Ep.: 480, Tot.St.: 129451, Avg.Num.St.: 273.5, Min-Max.Sc.: (-2.49, 0.02), Avg.Score: -0.744, Time: 01:26:29\n",
      "Ep.: 490, Tot.St.: 133017, Avg.Num.St.: 289.1, Min-Max.Sc.: (-3.00, 0.02), Avg.Score: -0.772, Time: 01:28:53\n",
      "Ep.: 500, Tot.St.: 135829, Avg.Num.St.: 291.7, Min-Max.Sc.: (-3.00, 0.02), Avg.Score: -0.751, Time: 01:30:46\n",
      "Ep.: 510, Tot.St.: 138896, Avg.Num.St.: 298.0, Min-Max.Sc.: (-3.00, 0.02), Avg.Score: -0.745, Time: 01:32:50\n",
      "Ep.: 520, Tot.St.: 141538, Avg.Num.St.: 300.0, Min-Max.Sc.: (-3.00, 0.02), Avg.Score: -0.744, Time: 01:34:36\n",
      "Ep.: 530, Tot.St.: 143954, Avg.Num.St.: 297.8, Min-Max.Sc.: (-3.00, 0.02), Avg.Score: -0.730, Time: 01:36:13\n",
      "Ep.: 540, Tot.St.: 146638, Avg.Num.St.: 298.1, Min-Max.Sc.: (-3.00, 0.02), Avg.Score: -0.737, Time: 01:38:01\n",
      "Ep.: 550, Tot.St.: 148892, Avg.Num.St.: 290.2, Min-Max.Sc.: (-3.00, 0.00), Avg.Score: -0.708, Time: 01:39:31\n",
      "Ep.: 560, Tot.St.: 151327, Avg.Num.St.: 285.4, Min-Max.Sc.: (-3.00, 0.00), Avg.Score: -0.674, Time: 01:41:09\n",
      "Ep.: 570, Tot.St.: 154018, Avg.Num.St.: 273.4, Min-Max.Sc.: (-3.00, 0.00), Avg.Score: -0.648, Time: 01:42:58\n",
      "Ep.: 580, Tot.St.: 157744, Avg.Num.St.: 282.9, Min-Max.Sc.: (-3.00, 0.00), Avg.Score: -0.646, Time: 01:45:27\n",
      "Ep.: 590, Tot.St.: 161049, Avg.Num.St.: 280.3, Min-Max.Sc.: (-1.99, 0.00), Avg.Score: -0.624, Time: 01:47:38\n",
      "Ep.: 600, Tot.St.: 164859, Avg.Num.St.: 290.3, Min-Max.Sc.: (-1.99, -0.05), Avg.Score: -0.638, Time: 01:50:10\n",
      "Ep.: 610, Tot.St.: 167256, Avg.Num.St.: 283.6, Min-Max.Sc.: (-1.99, -0.05), Avg.Score: -0.620, Time: 01:51:46\n",
      "Ep.: 620, Tot.St.: 169512, Avg.Num.St.: 279.7, Min-Max.Sc.: (-1.99, -0.05), Avg.Score: -0.609, Time: 01:53:15\n",
      "Ep.: 630, Tot.St.: 173476, Avg.Num.St.: 295.2, Min-Max.Sc.: (-1.99, -0.05), Avg.Score: -0.606, Time: 01:55:53\n",
      "Ep.: 640, Tot.St.: 177358, Avg.Num.St.: 307.2, Min-Max.Sc.: (-1.62, -0.05), Avg.Score: -0.604, Time: 01:58:27\n",
      "Ep.: 650, Tot.St.: 180530, Avg.Num.St.: 316.4, Min-Max.Sc.: (-1.62, -0.01), Avg.Score: -0.613, Time: 02:00:34\n",
      "Ep.: 660, Tot.St.: 183461, Avg.Num.St.: 321.3, Min-Max.Sc.: (-1.62, -0.01), Avg.Score: -0.599, Time: 02:02:32\n",
      "Ep.: 670, Tot.St.: 187719, Avg.Num.St.: 337.0, Min-Max.Sc.: (-1.80, -0.01), Avg.Score: -0.608, Time: 02:05:20\n",
      "Ep.: 680, Tot.St.: 191487, Avg.Num.St.: 337.4, Min-Max.Sc.: (-1.80, -0.01), Avg.Score: -0.572, Time: 02:07:50\n",
      "Ep.: 690, Tot.St.: 195712, Avg.Num.St.: 346.6, Min-Max.Sc.: (-1.80, 0.00), Avg.Score: -0.573, Time: 02:10:38\n",
      "Ep.: 700, Tot.St.: 199397, Avg.Num.St.: 345.4, Min-Max.Sc.: (-1.80, 0.00), Avg.Score: -0.539, Time: 02:13:04\n",
      "Ep.: 710, Tot.St.: 201474, Avg.Num.St.: 342.2, Min-Max.Sc.: (-1.80, 0.00), Avg.Score: -0.510, Time: 02:14:28\n",
      "Ep.: 720, Tot.St.: 205127, Avg.Num.St.: 356.1, Min-Max.Sc.: (-1.80, 0.00), Avg.Score: -0.488, Time: 02:16:54\n",
      "Ep.: 730, Tot.St.: 207607, Avg.Num.St.: 341.3, Min-Max.Sc.: (-1.80, 0.10), Avg.Score: -0.442, Time: 02:18:33\n",
      "Ep.: 740, Tot.St.: 210886, Avg.Num.St.: 335.3, Min-Max.Sc.: (-1.80, 0.26), Avg.Score: -0.376, Time: 02:20:45\n",
      "Ep.: 750, Tot.St.: 214010, Avg.Num.St.: 334.8, Min-Max.Sc.: (-1.80, 0.26), Avg.Score: -0.325, Time: 02:22:50\n",
      "Ep.: 760, Tot.St.: 217295, Avg.Num.St.: 338.3, Min-Max.Sc.: (-1.80, 0.59), Avg.Score: -0.266, Time: 02:25:01\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ep.: 770, Tot.St.: 220032, Avg.Num.St.: 323.1, Min-Max.Sc.: (-1.64, 0.59), Avg.Score: -0.191, Time: 02:26:51\n",
      "Ep.: 780, Tot.St.: 222383, Avg.Num.St.: 309.0, Min-Max.Sc.: (-1.64, 0.59), Avg.Score: -0.147, Time: 02:28:27\n",
      "Ep.: 790, Tot.St.: 224814, Avg.Num.St.: 291.0, Min-Max.Sc.: (-0.85, 0.59), Avg.Score: -0.073, Time: 02:30:04\n",
      "Ep.: 800, Tot.St.: 226893, Avg.Num.St.: 275.0, Min-Max.Sc.: (-0.54, 0.59), Avg.Score: -0.013, Time: 02:31:28\n",
      "Ep.: 810, Tot.St.: 229439, Avg.Num.St.: 279.6, Min-Max.Sc.: (-0.54, 0.59), Avg.Score: 0.036, Time: 02:33:10\n",
      "Ep.: 820, Tot.St.: 232008, Avg.Num.St.: 268.8, Min-Max.Sc.: (-0.39, 0.75), Avg.Score: 0.103, Time: 02:34:52\n",
      "Ep.: 830, Tot.St.: 233821, Avg.Num.St.: 262.1, Min-Max.Sc.: (-0.39, 0.75), Avg.Score: 0.143, Time: 02:36:05\n",
      "Ep.: 840, Tot.St.: 236507, Avg.Num.St.: 256.2, Min-Max.Sc.: (-0.39, 0.95), Avg.Score: 0.191, Time: 02:37:52\n",
      "Ep.: 850, Tot.St.: 238703, Avg.Num.St.: 246.9, Min-Max.Sc.: (-0.11, 0.95), Avg.Score: 0.232, Time: 02:39:21\n",
      "Ep.: 860, Tot.St.: 241160, Avg.Num.St.: 238.7, Min-Max.Sc.: (-0.11, 0.95), Avg.Score: 0.270, Time: 02:40:59\n",
      "Ep.: 870, Tot.St.: 243350, Avg.Num.St.: 233.2, Min-Max.Sc.: (-0.10, 0.95), Avg.Score: 0.319, Time: 02:42:26\n",
      "Ep.: 880, Tot.St.: 244919, Avg.Num.St.: 225.4, Min-Max.Sc.: (-0.10, 1.58), Avg.Score: 0.346, Time: 02:43:29\n",
      "Ep.: 890, Tot.St.: 246807, Avg.Num.St.: 219.9, Min-Max.Sc.: (-0.08, 1.58), Avg.Score: 0.388, Time: 02:44:45\n",
      "Ep.: 900, Tot.St.: 248874, Avg.Num.St.: 219.8, Min-Max.Sc.: (0.00, 1.58), Avg.Score: 0.430, Time: 02:46:08\n",
      "Ep.: 910, Tot.St.: 250647, Avg.Num.St.: 212.1, Min-Max.Sc.: (0.00, 1.58), Avg.Score: 0.450, Time: 02:47:18\n",
      "Ep.: 920, Tot.St.: 252422, Avg.Num.St.: 204.1, Min-Max.Sc.: (0.00, 1.58), Avg.Score: 0.460, Time: 02:48:30\n",
      "Ep.: 930, Tot.St.: 254362, Avg.Num.St.: 205.4, Min-Max.Sc.: (0.00, 1.58), Avg.Score: 0.489, Time: 02:49:48\n",
      "Ep.: 940, Tot.St.: 257054, Avg.Num.St.: 205.5, Min-Max.Sc.: (0.00, 1.91), Avg.Score: 0.534, Time: 02:51:37\n",
      "Ep.: 950, Tot.St.: 259119, Avg.Num.St.: 204.2, Min-Max.Sc.: (0.00, 1.91), Avg.Score: 0.570, Time: 02:52:59\n",
      "Ep.: 960, Tot.St.: 260719, Avg.Num.St.: 195.6, Min-Max.Sc.: (0.00, 1.91), Avg.Score: 0.575, Time: 02:54:03\n",
      "Ep.: 970, Tot.St.: 262890, Avg.Num.St.: 195.4, Min-Max.Sc.: (0.02, 1.91), Avg.Score: 0.601, Time: 02:55:29\n",
      "Ep.: 980, Tot.St.: 264935, Avg.Num.St.: 200.2, Min-Max.Sc.: (0.02, 1.91), Avg.Score: 0.630, Time: 02:56:51\n",
      "Ep.: 990, Tot.St.: 267509, Avg.Num.St.: 207.0, Min-Max.Sc.: (0.02, 1.91), Avg.Score: 0.667, Time: 02:58:35\n",
      "Ep.: 1000, Tot.St.: 269971, Avg.Num.St.: 211.0, Min-Max.Sc.: (0.02, 1.91), Avg.Score: 0.686, Time: 03:00:14\n",
      "Ep.: 1010, Tot.St.: 271420, Avg.Num.St.: 207.7, Min-Max.Sc.: (0.03, 1.91), Avg.Score: 0.691, Time: 03:01:12\n",
      "Ep.: 1020, Tot.St.: 273831, Avg.Num.St.: 214.1, Min-Max.Sc.: (0.03, 1.94), Avg.Score: 0.724, Time: 03:02:48\n",
      "Ep.: 1030, Tot.St.: 275505, Avg.Num.St.: 211.4, Min-Max.Sc.: (0.03, 2.20), Avg.Score: 0.729, Time: 03:03:55\n",
      "Ep.: 1040, Tot.St.: 277822, Avg.Num.St.: 207.7, Min-Max.Sc.: (0.03, 2.20), Avg.Score: 0.720, Time: 03:05:27\n",
      "Ep.: 1050, Tot.St.: 279943, Avg.Num.St.: 208.2, Min-Max.Sc.: (0.03, 2.20), Avg.Score: 0.719, Time: 03:06:51\n",
      "Ep.: 1060, Tot.St.: 282463, Avg.Num.St.: 217.4, Min-Max.Sc.: (0.05, 2.20), Avg.Score: 0.746, Time: 03:08:32\n",
      "Ep.: 1070, Tot.St.: 285324, Avg.Num.St.: 224.3, Min-Max.Sc.: (0.05, 2.20), Avg.Score: 0.766, Time: 03:10:27\n",
      "Ep.: 1080, Tot.St.: 287606, Avg.Num.St.: 226.7, Min-Max.Sc.: (0.04, 2.20), Avg.Score: 0.757, Time: 03:12:00\n",
      "Ep.: 1090, Tot.St.: 290135, Avg.Num.St.: 226.3, Min-Max.Sc.: (0.04, 2.20), Avg.Score: 0.741, Time: 03:13:43\n",
      "Ep.: 1100, Tot.St.: 293005, Avg.Num.St.: 230.3, Min-Max.Sc.: (0.04, 2.20), Avg.Score: 0.758, Time: 03:15:38\n",
      "Ep.: 1110, Tot.St.: 296204, Avg.Num.St.: 247.8, Min-Max.Sc.: (0.04, 2.20), Avg.Score: 0.792, Time: 03:17:46\n",
      "Ep.: 1120, Tot.St.: 299342, Avg.Num.St.: 255.1, Min-Max.Sc.: (0.04, 2.20), Avg.Score: 0.805, Time: 03:19:52\n",
      "Ep.: 1130, Tot.St.: 301934, Avg.Num.St.: 264.3, Min-Max.Sc.: (0.04, 2.14), Avg.Score: 0.814, Time: 03:21:36\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Ep.: 1530, Tot.St.: 400455, Avg.Num.St.: 341.2, Min-Max.Sc.: (0.03, 2.82), Avg.Score: 1.026, Time: 04:29:44\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Ep.: 2290, Tot.St.: 648172, Avg.Num.St.: 175.5, Min-Max.Sc.: (0.03, 3.12), Avg.Score: 1.038, Time: 07:32:25\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ep.: 3050, Tot.St.: 773357, Avg.Num.St.: 170.2, Min-Max.Sc.: (0.10, 3.43), Avg.Score: 1.058, Time: 09:10:35\n",
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      "Ep.: 3080, Tot.St.: 777650, Avg.Num.St.: 162.7, Min-Max.Sc.: (0.10, 3.43), Avg.Score: 0.999, Time: 09:14:00\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Ep.: 3810, Tot.St.: 966773, Avg.Num.St.: 388.5, Min-Max.Sc.: (0.17, 5.49), Avg.Score: 2.701, Time: 11:44:28\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ep.: 4570, Tot.St.: 1282713, Avg.Num.St.: 400.6, Min-Max.Sc.: (1.01, 6.58), Avg.Score: 3.241, Time: 16:26:44\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Ep.: 5330, Tot.St.: 1577192, Avg.Num.St.: 396.6, Min-Max.Sc.: (0.21, 7.11), Avg.Score: 3.133, Time: 22:19:27\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ep.: 6850, Tot.St.: 2138465, Avg.Num.St.: 368.3, Min-Max.Sc.: (0.34, 8.44), Avg.Score: 3.412, Time: 34:28:01\n",
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     ]
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     "text": [
      "Ep.: 7610, Tot.St.: 2401171, Avg.Num.St.: 339.8, Min-Max.Sc.: (0.33, 8.32), Avg.Score: 3.116, Time: 40:18:35\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Ep.: 9120, Tot.St.: 2830125, Avg.Num.St.: 264.6, Min-Max.Sc.: (0.26, 9.20), Avg.Score: 2.560, Time: 49:59:29\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ep.: 12120, Tot.St.: 3662678, Avg.Num.St.: 305.4, Min-Max.Sc.: (0.93, 10.82), Avg.Score: 3.348, Time: 68:57:10\n",
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      "Ep.: 12680, Tot.St.: 3859561, Avg.Num.St.: 364.8, Min-Max.Sc.: (0.57, 13.13), Avg.Score: 3.915, Time: 73:27:17\n",
      "Ep.: 12690, Tot.St.: 3862468, Avg.Num.St.: 357.0, Min-Max.Sc.: (0.57, 13.13), Avg.Score: 3.749, Time: 73:31:19\n",
      "Ep.: 12700, Tot.St.: 3866279, Avg.Num.St.: 362.1, Min-Max.Sc.: (0.57, 13.13), Avg.Score: 3.797, Time: 73:36:33\n",
      "Ep.: 12710, Tot.St.: 3869907, Avg.Num.St.: 355.1, Min-Max.Sc.: (0.57, 13.13), Avg.Score: 3.738, Time: 73:41:33\n",
      "Ep.: 12720, Tot.St.: 3874521, Avg.Num.St.: 368.4, Min-Max.Sc.: (0.57, 13.13), Avg.Score: 3.897, Time: 73:47:54\n",
      "Ep.: 12730, Tot.St.: 3878994, Avg.Num.St.: 375.8, Min-Max.Sc.: (0.26, 13.13), Avg.Score: 3.970, Time: 73:54:04\n",
      "Ep.: 12740, Tot.St.: 3882199, Avg.Num.St.: 359.3, Min-Max.Sc.: (0.26, 11.43), Avg.Score: 3.804, Time: 73:58:27\n",
      "Ep.: 12750, Tot.St.: 3886228, Avg.Num.St.: 362.6, Min-Max.Sc.: (0.26, 11.43), Avg.Score: 3.880, Time: 74:03:59\n",
      "Ep.: 12760, Tot.St.: 3891571, Avg.Num.St.: 380.0, Min-Max.Sc.: (0.26, 12.11), Avg.Score: 4.117, Time: 74:11:20\n",
      "Ep.: 12770, Tot.St.: 3894747, Avg.Num.St.: 380.2, Min-Max.Sc.: (0.26, 12.11), Avg.Score: 4.204, Time: 74:15:42\n",
      "Ep.: 12780, Tot.St.: 3899776, Avg.Num.St.: 402.1, Min-Max.Sc.: (0.26, 12.11), Avg.Score: 4.503, Time: 74:22:39\n",
      "Ep.: 12790, Tot.St.: 3903794, Avg.Num.St.: 413.3, Min-Max.Sc.: (0.26, 12.11), Avg.Score: 4.695, Time: 74:28:13\n",
      "Ep.: 12800, Tot.St.: 3906399, Avg.Num.St.: 401.2, Min-Max.Sc.: (0.26, 12.11), Avg.Score: 4.539, Time: 74:31:50\n",
      "Ep.: 12810, Tot.St.: 3909642, Avg.Num.St.: 397.4, Min-Max.Sc.: (0.26, 12.11), Avg.Score: 4.451, Time: 74:36:19\n",
      "Ep.: 12820, Tot.St.: 3912919, Avg.Num.St.: 384.0, Min-Max.Sc.: (0.26, 12.11), Avg.Score: 4.304, Time: 74:40:52\n",
      "Ep.: 12830, Tot.St.: 3917483, Avg.Num.St.: 384.9, Min-Max.Sc.: (1.18, 12.11), Avg.Score: 4.351, Time: 74:47:11\n",
      "Ep.: 12840, Tot.St.: 3921720, Avg.Num.St.: 395.2, Min-Max.Sc.: (1.18, 12.11), Avg.Score: 4.434, Time: 74:53:04\n",
      "Ep.: 12850, Tot.St.: 3927608, Avg.Num.St.: 413.8, Min-Max.Sc.: (0.56, 12.51), Avg.Score: 4.612, Time: 75:01:13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ep.: 12860, Tot.St.: 3933131, Avg.Num.St.: 415.6, Min-Max.Sc.: (0.34, 12.51), Avg.Score: 4.642, Time: 75:08:51\n",
      "Ep.: 12870, Tot.St.: 3938640, Avg.Num.St.: 438.9, Min-Max.Sc.: (0.34, 12.51), Avg.Score: 4.924, Time: 75:16:29\n",
      "Ep.: 12880, Tot.St.: 3942984, Avg.Num.St.: 432.1, Min-Max.Sc.: (0.34, 12.51), Avg.Score: 4.845, Time: 75:22:32\n",
      "Solved environment with Avg Score:   5.022619391295142\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from  collections  import deque\n",
    "\n",
    "def sac_train(max_steps):\n",
    "\n",
    "    total_numsteps = 0\n",
    "    updates = 0\n",
    "    num_episodes = 40000\n",
    "    updates=0\n",
    "\n",
    "    time_start = time.time()\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores_array = []\n",
    "    avg_scores_array = [] \n",
    "    num_steps_deque = deque(maxlen=100)\n",
    "    avg_numm_steps_array = []\n",
    "    \n",
    "    for i_episode in range(num_episodes): \n",
    "        episode_reward = 0\n",
    "        episode_steps = 0\n",
    "        done = False\n",
    "        state = env.reset()\n",
    "\n",
    "        for step in range(max_steps):    \n",
    "            if start_steps > total_numsteps:\n",
    "                action = env.action_space.sample()  # Sample random action\n",
    "            else:\n",
    "                action = agent.select_action(state)  # Sample action from policy\n",
    "\n",
    "            if len(memory) > batch_size:\n",
    "                \n",
    "                # Update parameters of all the networks\n",
    "                agent.update_parameters(memory, batch_size, updates)\n",
    "\n",
    "                updates += 1\n",
    "\n",
    "            next_state, reward, done, _ = env.step(action) # Step\n",
    "            episode_steps += 1\n",
    "            total_numsteps += 1\n",
    "            episode_reward += reward\n",
    "\n",
    "            mask = 1 if episode_steps == env._max_episode_steps else float(not done)\n",
    "\n",
    "            memory.push(state, action, reward, next_state, mask) # Append transition to memory\n",
    "\n",
    "            state = next_state\n",
    "            \n",
    "            if done:\n",
    "                break\n",
    "\n",
    "        scores_deque.append(episode_reward)\n",
    "        scores_array.append(episode_reward)        \n",
    "        avg_score = np.mean(scores_deque)\n",
    "        avg_scores_array.append(avg_score)\n",
    "        min_score = np.min(scores_deque)\n",
    "        max_score = np.max(scores_deque)\n",
    "        num_steps_deque.append(episode_steps)\n",
    "        avg_num_steps = np.mean(num_steps_deque)\n",
    "        avg_numm_steps_array.append(avg_num_steps)\n",
    "        \n",
    "        \n",
    "        if i_episode % 500 == 0 and i_episode > 0:\n",
    "            reward_round = round(episode_reward, 2)\n",
    "            save(agent, 'dir_duck_lr0.00003', 'weights', str(i_episode), str(reward_round))\n",
    "\n",
    "        s =  (int)(time.time() - time_start)\n",
    "          \n",
    "        if i_episode % 10 == 0 and i_episode > 0:  \n",
    "\n",
    "            print(\"Ep.: {}, Tot.St.: {}, Avg.Num.St.: {:.1f}, Min-Max.Sc.: ({:.2f}, {:.2f}), Avg.Score: {:.3f}, Time: {:02}:{:02}:{:02}\".\\\n",
    "                format(i_episode, total_numsteps, avg_num_steps, min_score, max_score, avg_score, \\\n",
    "                  s//3600, s%3600//60, s%60))\n",
    "\n",
    "                    \n",
    "        if (avg_score >= threshold):            \n",
    "            print('Solved environment with Avg Score:  ', avg_score)\n",
    "            \n",
    "            break;\n",
    "            \n",
    "    return scores_array, avg_scores_array, avg_numm_steps_array \n",
    "\n",
    "scores, avg_scores, avg_numm_steps = sac_train(max_steps=max_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "reward_round = round(np.max(scores), 2)\n",
    "save(agent, 'dir_duck_lr0.00003', 'weights', 'final', str(reward_round))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of scores:  12888 , len of avg_scores:  12888\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "print('length of scores: ', len(scores), ', len of avg_scores: ', len(avg_scores))\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(1, len(scores)+1), scores, label=\"Score\")\n",
    "plt.plot(np.arange(1, len(avg_scores)+1), avg_scores, label=\"Avg.Score on 100 episodes\")\n",
    "## plt.plot(np.arange(1, len(avg_numm_steps)+1), avg_numm_steps, label=\"Avg.Num.Steps on 100 episodes\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1)) \n",
    "plt.ylabel('Score')\n",
    "plt.xlabel('Episodes #')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure()\n",
    "ax = fig2.add_subplot(111)\n",
    "# plt.plot(np.arange(1, len(scores)+1), scores, label=\"Score\")\n",
    "# plt.plot(np.arange(1, len(avg_scores)+1), avg_scores, label=\"Avg.Score on 100 episodes\")\n",
    "plt.plot(np.arange(1, len(avg_numm_steps)+1), avg_numm_steps, label=\"Avg.Num.Steps on 100 episodes\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1)) \n",
    "plt.ylabel('Num.Steps')\n",
    "plt.xlabel('Episodes #')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def play(env, agent, num_episodes):\n",
    "    \n",
    "    state = env.reset()\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores = []\n",
    "    \n",
    "    for i_episode in range(num_episodes + 1):\n",
    "        \n",
    "        state = env.reset()\n",
    "        score = 0                    \n",
    "        time_start = time.time()\n",
    "        \n",
    "        while True:\n",
    "            \n",
    "            action = agent.select_action(state, eval=False)\n",
    "            env.render()\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            score += reward \n",
    "            state = next_state\n",
    "    \n",
    "            if done:\n",
    "                break\n",
    "                \n",
    "        s = (int)(time.time() - time_start)\n",
    "        \n",
    "        scores_deque.append(score)\n",
    "        scores.append(score)    \n",
    "        \n",
    "        print('Episode {}\\tAverage Score: {:.2f},\\tScore: {:.2f} \\tTime: {:02}:{:02}:{:02}'\\\n",
    "                  .format(i_episode, np.mean(scores_deque), score, s//3600, s%3600//60, s%60)) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pybullet_envs.bullet.minitaur_gym_env as e\n",
    "#env_render = e.MinitaurBulletEnv(render=True)\n",
    "#play(env=env_render, agent=agent, num_episodes=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
